code: def run(self, face, audio_seq, output_dir, visualization=True):
      if os.path.isfile(face) and path.basename(face).split(".")[1] in [
          "jpg",
          "png",
          "jpeg",
      ]:
          self.static = True
  
      if not os.path.isfile(face):
          raise ValueError(
              "--face argument must be a valid path to video/image file"
          )
  
      elif path.basename(face).split(".")[1] in ["jpg", "png", "jpeg"]:
          full_frames = [cv2.imread(face)]
          fps = self.fps
  
      else:
          video_stream = cv2.VideoCapture(face)
          fps = video_stream.get(cv2.CAP_PROP_FPS)
  
          print("Reading video frames...")
  
          full_frames = []
          while 1:
              still_reading, frame = video_stream.read()
              if not still_reading:
                  video_stream.release()
                  break
              if self.resize_factor > 1:
                  frame = cv2.resize(
                      frame,
                      (
                          frame.shape[1] // self.resize_factor,
                          frame.shape[0] // self.resize_factor,
                      ),
                  )
  
              if self.rotate:
                  frame = cv2.rotate(frame, cv2.cv2.ROTATE_90_CLOCKWISE)
  
              y1, y2, x1, x2 = self.crop
              if x2 == -1:
                  x2 = frame.shape[1]
              if y2 == -1:
                  y2 = frame.shape[0]
  
              frame = frame[y1:y2, x1:x2]
  
              full_frames.append(frame)
  
      print(
          "Number of frames available for inference: "
          + str(len(full_frames))
      )
  
      if not audio_seq.endswith(".wav"):
          print("Extracting raw audio...")
          command = "ffmpeg -y -i {} -strict -2 {}".format(
              audio_seq, "temp/temp.wav"
          )
  
          subprocess.call(command, shell=True)
          audio_seq = "temp/temp.wav"
  
      wav = audio.load_wav(audio_seq, 16000)
      mel = audio.melspectrogram(wav)
      if np.isnan(mel.reshape(-1)).sum() > 0:
          raise ValueError(
              "Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again"
          )
  
      mel_chunks = []
      mel_idx_multiplier = 80.0 / fps
      i = 0
      while 1:
          start_idx = int(i * mel_idx_multiplier)
          if start_idx + mel_step_size > len(mel[0]):
              mel_chunks.append(mel[:, len(mel[0]) - mel_step_size :])
              break
          mel_chunks.append(
              mel[:, start_idx : start_idx + mel_step_size]
          )
          i += 1
  
      print("Length of mel chunks: {}".format(len(mel_chunks)))
  
      full_frames = full_frames[: len(mel_chunks)]
  
      batch_size = self.wav2lip_batch_size
      gen = self.datagen(full_frames.copy(), mel_chunks)
  
      model = Wav2Lip()
      if self.checkpoint_path is None:
          model_weights_path = get_weights_path_from_url(
              WAV2LIP_WEIGHT_URL
          )
          weights = paddle.load(model_weights_path)
      else:
          weights = paddle.load(self.checkpoint_path)
      model.load_dict(weights)
      model.eval()
      print("Model loaded")
      for i, (img_batch, mel_batch, frames, coords) in enumerate(
          tqdm(
              gen,
              total=int(np.ceil(float(len(mel_chunks)) / batch_size)),
          )
      ):
          if i == 0:
              frame_h, frame_w = full_frames[0].shape[:-1]
              out = cv2.VideoWriter(
                  "temp/result.avi",
                  cv2.VideoWriter_fourcc(*"DIVX"),
                  fps,
                  (frame_w, frame_h),
              )
  
          img_batch = paddle.to_tensor(
              np.transpose(img_batch, (0, 3, 1, 2))
          ).astype("float32")
          mel_batch = paddle.to_tensor(
              np.transpose(mel_batch, (0, 3, 1, 2))
          ).astype("float32")
  
          with paddle.no_grad():
              pred = model(mel_batch, img_batch)
  
          pred = pred.numpy().transpose(0, 2, 3, 1) * 255.0
  
          for p, f, c in zip(pred, frames, coords):
              y1, y2, x1, x2 = c
              if self.face_enhancement:
                  p = self.faceenhancer.enhance_from_image(p)
              p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
  
              f[y1:y2, x1:x2] = p
              out.write(f)
  
      out.release()
      os.makedirs(output_dir, exist_ok=True)
      if visualization:
          command = "ffmpeg -y -i {} -i {} -strict -2 -q:v 1 {}".format(
              audio_seq,
              "temp/result.avi",
              os.path.join(output_dir, "result.avi"),
          )
          subprocess.call(command, shell=platform.system() != "Windows")
  
file_path: PaddleHub/modules/image/Image_gan/gan/wav2lip/model.py
imports: from os import listdir, path, makedirs
  import platform
  import numpy
  import scipy, cv2, os, sys, argparse
  import json, subprocess, random, string
  from tqdm import tqdm
  from glob import glob
  import paddle
  from paddle.utils.download import get_weights_path_from_url
  from ppgan.faceutils import face_detection
  from ppgan.utils import audio
  from ppgan.models.generators.wav2lip import Wav2Lip
label_desc: Use image node_id 5 for calling the Wav2LipPredictor obj's underlying member method code with example usage: obj.run(face, audio_seq, output_dir, visualization) without return types